SnapGo

What it does

SnapCo uses the SnapChat API to take a picture of a product, and using a deep learning neural network, classify the brand that the product is. The brand is then put on the snap as a filter/sticker. This is a great way for companies to advertise, and it is also easier for consumers to make a statement with their branded item purchases.

How we built it

We built an app with Swift on iOS that was the front end of the project. The back end was a deep learning model in AutoML in Google Cloud Console. The SnapKit Creation API was implemented in the app and was called every time a picture was taken in the app we created.

Challenges we ran into

The SnapKit API was very difficult to work with because of the limited amount of online resources that exist on it. The API was also very buggy and inconsistent. We were not able to implement the full functionality of the API. Additionally, finding a way to integrate our ML model with our app proved to be more difficult that we thought it would be. There was also limited online resources about this. Because of this, we could not integrate the model, but we still have a separate model.

Accomplishments that we're proud of

We did not have a lot of experience with iOS development, and we still managed to implement a lot of functionality. We also made a decent ML model that was mostly very accurate in its predictions.

What we learned

We learned that implementing API's can be very difficult. We also learned a lot about Swift, iOS development, and deep learning networks.

What's next for SnapGo

We will hopefully be able to implement all the features that we could not implement in the 24 hours we had to work on this project

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I helped with the connection from front end to backed by providing instructions for trying to connect the ML model and app to firebase. I also worked on android development using Kotlin and java for camera features.